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Implications of electrode modifications in aqueous organic redox flow batteries

Journal of Energy Chemistry 2024年第8期95卷 499-510页

作者： Zahid Manzoor Bhat Mohammad Furquan Muhammad Aurang Zeb Gul Sial Umair Alam Atif Saeed Alzahrani Mohammad qamar Interdisciplinary Research Center for Hydrogen Technologies and Carbon Management(IRC-HTCM) King Fahd University of Petroleum & Minerals(KFUPM) Department of Materials Science and Engineering King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Sustainable Energy Systems(IRC-SES) King Fahd University of Petroleum and Minerals

Aqueous organic redox flow batteries(RFBs) exhibit favorable characteristics, such as tunability, multielectron transfer capability, and stability of the redox active molecules utilized as anolytes and catholytes, making them very viable contenders for large-scale grid storage applications. Considerable attention has been paid on the development of efficient redox-active molecules and their performance optimization through chemical substitutions at various places on the backbone as part of the pursuit for high-performance RFBs. Despite the fact that electrodes are vital to optimal performance, they have not garnered significant attention. Limited research has been conducted on the effects of electrode modifications to improve the performance of RFBs. The primary emphasis has been given on the impact of electrode engineering to augment the efficiency of aqueous organic RFBs. An overview of electron transfer at the electrode–electrolyte interface is provided. The implications of electrode modification on the performance of redox flow batteries, with a particular focus on the anodic and cathodic half-cells separately, are then discussed. In each section, significant discrepancies surrounding the effects of electrode engineering are thoroughly examined and discussed. Finally, we have presented a comprehensive assessment along with our perspectives on the future trajectory.

关键词： Electrode modification Organic redox molecules Outer sphere and inner sphere Redox flow batteries

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A Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

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Computers, Materials & Continua 2024年第5期79卷 2945-2970页

作者： Muhammad Salman qamar Ihsan ulHaq Amil Daraz Atif MAlamri Salman A.AlQahtani Muhammad Fahad Munir Department of Electrical and Computer Engineering International Islamic UniversityIslamabad44000Pakistan School of Information Science and Engineering NingboTech UniversityNingbo315100China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing protocols.InWSNs,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable operation.WSN data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network traversal.The mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring RPs.The unique determination of this study is the shortest path to reach RPs.As the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static sinks.In this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the MS.Both methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide coverage.In addition,a method of using MS scheduling for efficient data collection is provided.Extensive simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

关键词： Wireless Sensor Networks(WSNs) mobile sink(MS) rendezvous point(RP) machine learning Artificial Neural Networks(ANNs)

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Dinitrophenyl-oxadiazole compounds:Design strategy,synthesis,and properties of a series of new melt-cast explosives

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Defence Technology（防务技术） 2024年第5期35卷 100-107页

作者： Bao-long Kuang Ting-wei Wang Cong Li Mou Sun qamar-un-Nisa Tariq Chao Zhang Zhi-ming Xie Zu-jia Lu Jian-guo Zhang State Key Laboratory of Explosion Science and Technology Beijing Institute of TechnologyBeijing100081China China Wuzhou Engineering Group CO. LTD.Beijing 100053China

Melt-cast explosives are the most widely used energetic materials in military composite explosives,researchers have been unremittingly exploring high-energy and insensitive melt-cast explosives.In this work,a series of dinitrophenyl-oxadiazole compounds were designed and prepared.These compounds have an ideal low melting point(80-97℃),good detonation performance(detonation velocity D=6455-6971 m/s,detonation pressure P=18-19 GPa)and extreme insensitive nature(impact sensitivity≥60 J,friction sensitivity>360 N).All these compounds were well characterized by nuclear magnetic resonance,fourier transform infrared spectroscopy,elemental analysis.Compounds 2,3 were unambiguously confirmed by X-ray single crystal diffraction analysis.As a result,their overall properties are superior to traditional melt-cast explosives trinitrotoluene(TNT)and dinitroanisole(DNAN)which may have excellent potential applications in insensitive melt-cast explosives.

关键词： Dinitrophenyl-oxadiazole Melt-cast explosive Low sensitivity

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Petrology and Petrographic Delineation of Kamlial Formation Sandstone Mong and Thorar Village, Northern Pakistan

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Journal of Geoscience and Environment Protection 2024年第5期12卷 302-324页

作者： Ehtisham Mehmood Haishen Lv Pei Gao Soban qamar The National Key Laboratory of Water Disaster Prevention College of Hydrology and Water Resources Hohai University Nanjing China College of Mechanical and Petroleum Engineering University of Engineering and Technology Taxila Pakistan

The origin of sandstone in the Rawalpindi group is disputed because of the lesser Himalayas complicated geological structure and ongoing tectonic activity. The goal of the study is to learn more about the petrographic and geological aspects of the Siwalik molasses deposits, which are formations that belong to the same age group. The Early Miocene Kamlial Formation, the Middle to Late Miocene Chinji Formation, and the Late Miocene Nagri Formation are the stratigraphic units revealed in the project area. The texture of the sandstone found in the Rawalpindi Group and Siwalik is fine to medium-grained. The hue ranges from grey to greenish grey. The sandstone displays thin to medium-bedded layers and exhibits thin lamination throughout. The sandstone of the Kamlial Formation contains load casts, potholes, worm burrows, hematite layers, and filled and unfilled mud cracks in basic structures. Model petrographic research reveals that the Murree Formation primarily consists of light minerals like feldspar, quartzite, and felice, whereas the Kamlial Formation is composed of heavy minerals like garnet and tourmaline. Sandstone from the Rawalpindi group undergoes analysis to ascertain its provenance using the quartz feldspar lithic fragments ternary diagram technique. Each plot in the QFL diagram’s recycled orogeny provenance field is plotted.

关键词： Hazara Kashmir Syntaxis Rawalpindi Group Siwalik Group Himalayas Neogene

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ABMRF:An Ensemble Model for Author Profiling Based on Stylistic Features Using Roman Urdu

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Intelligent Automation & Soft Computing 2024年第2期39卷 301-317页

作者： Aiman Muhammad Arshad Bilal Khan Khalil Khan Ali Mustafa qamar Rehan Ullah Khan Department of Computer Science City University of Science and Information TechnologyPeshawarPakistan Department of Computer Science School of Engineering and Digital SciencesNazarbayev UniversityAstanaKazakhstan Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia

This study explores the area of Author Profiling(AP)and its importance in several industries,including forensics,security,marketing,and education.A key component of AP is the extraction of useful information from text,with an emphasis on the writers’ages and genders.To improve the accuracy of AP tasks,the study develops an ensemble model dubbed ABMRF that combines AdaBoostM1(ABM1)and Random Forest(RF).The work uses an extensive technique that involves textmessage dataset pretreatment,model training,and assessment.To evaluate the effectiveness of several machine learning(ML)algorithms in classifying age and gender,including Composite Hypercube on Random Projection(CHIRP),Decision Trees(J48),Na飗e Bayes(NB),K Nearest Neighbor,AdaboostM1,NB-Updatable,RF,andABMRF,they are compared.The findings demonstrate thatABMRFregularly beats the competition,with a gender classification accuracy of 71.14%and an age classification accuracy of 54.29%,respectively.Additional metrics like precision,recall,F-measure,Matthews Correlation Coefficient(MCC),and accuracy support ABMRF’s outstanding performance in age and gender profiling tasks.This study demonstrates the usefulness of ABMRF as an ensemble model for author profiling and highlights its possible uses in marketing,law enforcement,and education.The results emphasize the effectiveness of ensemble approaches in enhancing author profiling task accuracy,particularly when it comes to age and gender identification.

关键词： Machine learning author profiling AdaBoostM1 random forest ensemble learning text classification

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Testing Procedure for Laboratory Scale Semi Cryogenic Combustion Chamber of LPRE with Problems Faced and Lessons Learned

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Journal of Thermal Science 2022年第6期31卷 2171-2177页

作者： KHAN Taj Wali qamar Ihtzaz Department of Aeronautics and Astronautics Institute of Space TechnologyIslamabad 44000Pakistan

Design, development, and testing of LPRE(Liquid Propellant Rocket Engine) are difficult and expensive tasks. Prior to full-scale design, it is indispensable to optimize important parameters at sub-scale. Propellants flow rates are low for a sub-scale or laboratory scale combustion chambers. It is hard to satisfy chamber cooling and chill feed lines quickly with low flow rates of propellants. This paper proposes a detailed procedure for testing of a laboratory scale semi-cryogenic combustion chamber. Many tests were conducted with a small scale adjustable length combustion chamber. The injection head of the chamber was interchangeable. Liquid-liquid pressure swirl injector and like impinging injectors were used with two different injection heads. Liquid oxygen and kerosene were used as oxidizer and fuel, respectively. Oxidizer to fuel mixing ratio was 0.29-0.45 and the total propellant mass flow rate was 0.06-0.1 kg/s. Problems were faced during testing, including, explosion in the combustion chamber, fuel injector blockage, unstable combustion, incomplete chilling and blockage of cooling water channel, etc. A detailed procedure is designed on the basis of the lessons learned which was experimentally proved.

关键词： LPRE cryogenic testing procedure combustion chamber

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Deep Convolutional Neural Network Based Churn Prediction for Telecommunication Industry

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Computer Systems Science & Engineering 2022年第12期43卷 1255-1270页

作者： Nasebah Almufadi Ali Mustafa qamar Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computing School of Electrical Engineering and Computer ScienceNational University of Sciences and Technology(NUST)IslamabadPakistan

Currently,mobile communication is one of the widely used means of communication.Nevertheless,it is quite challenging for a telecommunication company to attract new customers.The recent concept of mobile number portability has also aggravated the problem of customer churn.Companies need to identify beforehand the customers,who could potentially churn out to the competitors.In the telecommunication industry,such identification could be done based on call detail records.This research presents an extensive experimental study based on various deep learning models,such as the 1D convolutional neural network(CNN)model along with the recurrent neural network(RNN)and deep neural network(DNN)for churn prediction.We use the mobile telephony churn prediction dataset obtained from ***,containing the data for around 100,000 individuals,out of which 86,000 are non-churners,whereas 14,000 are churned customers.The imbalanced data are handled using undersampling and oversampling.The accuracy for CNN,RNN,and DNN is 91%,93%,and 96%,respectively.Furthermore,DNN got 99%for ROC.

关键词： Deep learning machine learning churn prediction convolutional neural network recurrent neural network

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Data-Driven Load Forecasting Using Machine Learning and Meteorological Data

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Computer Systems Science & Engineering 2023年第3期44卷 1973-1988页

作者： Aishah Alrashidi Ali Mustafa qamar Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia

Electrical load forecasting is very crucial for electrical power systems’planning and operation.Both electrical buildings’load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load forecasting.The meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh,Saudi Arabia.The factory load and meteorological data used in this study are recorded hourly between 2016 and 2017.These data are provided by King Abdullah City for Atomic and Renewable Energy and Saudi Electricity Company at a site located in Riyadh.After applying the data pre-processing techniques to prepare the data,different machine learning algorithms,namely Artificial Neural Network and Support Vector Regression(SVR),are applied and compared to predict the factory load.In addition,for the sake of selecting the optimal set of features,13 different combinations of features are investigated in this study.The outcomes of this study emphasize selecting the optimal set of features as more features may add complexity to the learning process.Finally,the SVR algorithm with six features provides the most accurate prediction values to predict the factory load.

关键词： Electricity load forecasting meteorological data machine learning feature selection modeling real-world problems predictive analytics

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3D Printed Integrated Gradient-Conductive MXene/CNT/Polyimide Aerogel Frames for Electromagnetic Interference Shielding with Ultra-Low Reflection

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Nano-Micro Letters 2023年第3期15卷 332-345页

作者： Tiantian Xue Yi Yang Dingyi Yu qamar Wali Zhenyu Wang Xuesong Cao Wei Fan Tianxi Liu State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and EngineeringDonghua University2999 North Renmin RoadShanghai 201620People’s Republic of China Key Laboratory of Synthetic and Biological Colloids Ministry of EducationSchool of Chemical and Material EngineeringJiangnan UniversityWuxi 214122People’s Republic of China Institute of Environmental Processes and Pollution Control School of Environment and Civil EngineeringJiangnan UniversityWuxi 214122People’s Republic of China NUTECH School of Applied Sciences&Humanities National University of TechnologyIslamabad 44000Pakistan

Construction of advanced electromagnetic interference(EMI)shielding materials with miniaturized,programmable structure and low reflection are promising but challenging.Herein,an integrated transition-metal carbides/carbon nanotube/polyimide(gradient-conductive MXene/CNT/PI,GCMCP)aerogel frame with hierarchical porous structure and gradient-conductivity has been constructed to achieve EMI shielding with ultra-low reflection.The gradient-conductive structures are obtained by continuous 3D printing of MXene/CNT/poly(amic acid)inks with different CNT contents,where the slightly conductive top layer serves as EM absorption layer and the highly conductive bottom layer as reflection layer.In addition,the hierarchical porous structure could extend the EM dissipation path and dissipate EM by multiple reflections.Consequently,the GCMCP aerogel frames exhibit an excellent average EMI shielding efficiency(68.2 dB)and low reflection(R=0.23).Furthermore,the GCMCP aerogel frames with miniaturized and programmable structures can be used as EMI shielding gaskets and effectively block wireless power transmission,which shows a prosperous application prospect in defense industry and aerospace.

关键词： 3D printing MXene/CNT/Polyimide aerogel Gradient-conductive Electromagnetic interference shielding

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Development of a low-frequency magnetic lightning mapping system(LFM-LMS)in North China:validation and preliminary results

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Earth and Planetary Physics 2023年第4期7卷 460-470页

作者： Xiao Li GaoPeng Lu FanChao Lyu HongBo Zhang Kainat qamar RuBin Jiang School of Earth and Space Sciences University of Science and Technology of ChinaHefei 230026China Key Laboratory of Atmospheric Optics Anhui Institute of Optics and Fine MechanicsHFIPSChinese Academy of SciencesHefei 230031China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science&TechnologyNanjing 210044China Key Laboratory of Transportation Meteorology of China Meteorological Administration Nanjing Joint Institute for Atmospheric SciencesNanjing 210000China Key Laboratory for Middle Atmosphere and Global Environment Observation Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China

A low-frequency magnetic lightning mapping system(LFM-LMS)was built during the SHAndong Triggered Lightning Experiment(SHATLE),based on continuous measurements of magnetic field radiation from lightning.The hardware and source-mapping techniques used by the LFM-LMS were introduced;both Monte Carlo simulations and the observation of rocket-triggered lightning examples were employed to examine the location accuracy and detection effectiveness of the LFM-LMS.We estimated that the system’s location accuracy about 100−200 m horizontally and~200 m vertically.A natural intra-cloud lightning flash and a rocket-triggered lightning flash,both with intricate structures and discharging processes,were examined using the three-dimensional mapping results.The progressing path of negative lightning leaders is usually well-defined,and its propagation speed is estimated to be(0.5−1.4)×10^(6)m/s.In summary,the LFM-LMS can reconstruct the three-dimensional morphology of lightning flashes;this technology provides a efficient method for investigating the characteristics of lightning development,as well as the overall electrical strucuture of thunderstorms.

关键词： rocket-triggered lightning low-frequency magnetic field lightning mapping observation location accuracy

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